In modern showjumping and cross-country riding, the success of the horse-rider-pair is measured by the ability to finish a given course of obstacles without penalties within a given time. A horse performs a successful (penalty-free) jump, if no element of the fence falls during the jump. The success of each jump is determined by the correct take-off point of the horse in front of the fence and the amount of strides a horse does between fences. This paper proposes a solution for tracking gaits and jumps using a smartphone attached to the horse's saddle. We propose an event detection algorithm based on Discrete Wavelet Transform and a peak detection to detect jumps and canter strides between fences. We segment the signal to find gait and jump sections, evaluate statistical and heuristic features and classify the segments using different machine learning algorithms. We show that horse jumps and canter strides are detected with a precision of 94.6% and 89.8% recall. All gaits and jumps are further classified with an accuracy of up to 95.4% and a Kappa coefficient (KC) of up to 93%.